





import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


_A					:				Optional[int]				=			"""\

"""

_A					:				Any				=			"""
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.

For more information, see https://huggingface.co/docs/transformers/perplexity
"""

_A					:				List[str]				=			"""
Args:
    model_id (str): model used for calculating Perplexity
            NOTE: Perplexity can only be calculated for causal language models.
                    This includes models such as gpt2, causal variations of bert,
                    causal versions of t5, and more (the full list can be found
                    in the AutoModelForCausalLM documentation here:
                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )

    input_texts (list of str): input text, each separate text snippet
        is one list entry.
    batch_size (int): the batch size to run texts through the model. Defaults to 16.
    add_start_token (bool): whether to add the start token to the texts,
        so the perplexity can include the probability of the first word. Defaults to True.
    device (str): device to run on, defaults to 'cuda' when available
Returns:
    perplexity: dictionary containing the perplexity scores for the texts
        in the input list, as well as the mean perplexity. If one of the input texts is
        longer than the max input length of the model, then it is truncated to the
        max length for the perplexity computation.
Examples:
    Example 1:
        >>> perplexity = datasets.load_metric(\"perplexity\")
        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]
        >>> results = perplexity.compute(model_id='gpt2',
        ...                              add_start_token=False,
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        ['perplexities', 'mean_perplexity']
        >>> print(round(results[\"mean_perplexity\"], 2))
        78.22
        >>> print(round(results[\"perplexities\"][0], 2))
        11.11

    Example 2:
        >>> perplexity = datasets.load_metric(\"perplexity\")
        >>> input_texts = datasets.load_dataset(\"wikitext\",
        ...                                     \"wikitext-2-raw-v1\",
        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS
        [...]
        >>> input_texts = [s for s in input_texts if s!='']
        >>> results = perplexity.compute(model_id='gpt2',
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        ['perplexities', 'mean_perplexity']
        >>> print(round(results[\"mean_perplexity\"], 2))
        60.35
        >>> print(round(results[\"perplexities\"][0], 2))
        81.12
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,     _KWARGS_DESCRIPTION    )
class 					__snake_case	(  datasets.Metric    ):


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

        return datasets.MetricInfo(
            description=_DESCRIPTION   ,				citation=_CITATION   ,				inputs_description=_KWARGS_DESCRIPTION   ,				features=datasets.Features(
                {
                    '''input_texts''': datasets.Value('''string'''     ),
                }     )   ,				reference_urls=['''https://huggingface.co/docs/transformers/perplexity''']   ,				)




    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_ = 16   ,				A_ = True   ,				A_=None     ):





        '''simple docstring'''

        if device is not None:
            assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
            if device == "gpu":
                SCREAMING_SNAKE_CASE__											=			'''cuda'''
        else:
            SCREAMING_SNAKE_CASE__											=			'''cuda''' if torch.cuda.is_available() else '''cpu'''

        SCREAMING_SNAKE_CASE__											=			AutoModelForCausalLM.from_pretrained(A_     )
        SCREAMING_SNAKE_CASE__											=			model.to(A_     )

        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained(A_     )

        # if batch_size > 1 (which generally leads to padding being required), and
        # if there is not an already assigned pad_token, assign an existing
        # special token to also be the padding token
        if tokenizer.pad_token is None and batch_size > 1:
            SCREAMING_SNAKE_CASE__											=			list(tokenizer.special_tokens_map_extended.values()     )
            # check that the model already has at least one special token defined
            assert (
                len(A_     ) > 0
            ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
            # assign one of the special tokens to also be the pad token
            tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]}     )

        if add_start_token:
            # leave room for <BOS> token to be added:
            assert (
                tokenizer.bos_token is not None
            ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
            SCREAMING_SNAKE_CASE__											=			model.config.max_length - 1
        else:
            SCREAMING_SNAKE_CASE__											=			model.config.max_length

        SCREAMING_SNAKE_CASE__											=			tokenizer(
            A_   ,				add_special_tokens=A_   ,				padding=A_   ,				truncation=A_   ,				max_length=A_   ,				return_tensors='''pt'''   ,				return_attention_mask=A_   ,				).to(A_     )

        SCREAMING_SNAKE_CASE__											=			encodings['''input_ids''']
        SCREAMING_SNAKE_CASE__											=			encodings['''attention_mask''']

        # check that each input is long enough:
        if add_start_token:
            assert torch.all(torch.ge(attn_masks.sum(1     )   ,				1     )     ), "Each input text must be at least one token long."
        else:
            assert torch.all(
                torch.ge(attn_masks.sum(1     )   ,				2     )     ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

        SCREAMING_SNAKE_CASE__											=			[]
        SCREAMING_SNAKE_CASE__											=			CrossEntropyLoss(reduction='''none'''     )

        for start_index in logging.tqdm(range(0   ,				len(A_     )   ,				A_     )     ):
            SCREAMING_SNAKE_CASE__											=			min(start_index + batch_size   ,				len(A_     )     )
            SCREAMING_SNAKE_CASE__											=			encoded_texts[start_index:end_index]
            SCREAMING_SNAKE_CASE__											=			attn_masks[start_index:end_index]

            if add_start_token:
                SCREAMING_SNAKE_CASE__											=			torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0     )     ).to(A_     )
                SCREAMING_SNAKE_CASE__											=			torch.cat([bos_tokens_tensor, encoded_batch]   ,				dim=1     )
                SCREAMING_SNAKE_CASE__											=			torch.cat(
                    [torch.ones(bos_tokens_tensor.size()   ,				dtype=torch.intaa     ).to(A_     ), attn_mask]   ,				dim=1     )

            SCREAMING_SNAKE_CASE__											=			encoded_batch

            with torch.no_grad():
                SCREAMING_SNAKE_CASE__											=			model(A_   ,				attention_mask=A_     ).logits

            SCREAMING_SNAKE_CASE__											=			out_logits[..., :-1, :].contiguous()
            SCREAMING_SNAKE_CASE__											=			labels[..., 1:].contiguous()
            SCREAMING_SNAKE_CASE__											=			attn_mask[..., 1:].contiguous()

            SCREAMING_SNAKE_CASE__											=			torch.expa(
                (loss_fct(shift_logits.transpose(1   ,				2     )   ,				A_     ) * shift_attention_mask_batch).sum(1     )
                / shift_attention_mask_batch.sum(1     )     )

            ppls += perplexity_batch.tolist()

        return {"perplexities": ppls, "mean_perplexity": np.mean(A_     )}



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       unittest.TestCase				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[int]								=	StableDiffusionInstructPixaPixPipeline
							UpperCAmelCase_						:		str								=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
							UpperCAmelCase_						:		List[Any]								=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							UpperCAmelCase_						:		Optional[int]								=	IMAGE_TO_IMAGE_IMAGE_PARAMS
							UpperCAmelCase_						:		Any								=	IMAGE_TO_IMAGE_IMAGE_PARAMS




							def       snake_case			(					self						)		->       List[Any]:
														torch.manual_seed(0						)
														A      :					Tuple      		=					UNetaDConditionModel(
														    block_out_channels=(32, 64)    ,						layers_per_block=2    ,						sample_size=32    ,						in_channels=8    ,						out_channels=4    ,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,						up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,						cross_attention_dim=32    ,						)
														A      :					Any      		=					PNDMScheduler(skip_prk_steps=__UpperCAmelCase						)
														torch.manual_seed(0						)
														A      :					Dict      		=					AutoencoderKL(
														    block_out_channels=[32, 64]    ,						in_channels=3    ,						out_channels=3    ,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,						latent_channels=4    ,						)
														torch.manual_seed(0						)
														A      :					str      		=					CLIPTextConfig(
														    bos_token_id=0    ,						eos_token_id=2    ,						hidden_size=32    ,						intermediate_size=37    ,						layer_norm_eps=1E-05    ,						num_attention_heads=4    ,						num_hidden_layers=5    ,						pad_token_id=1    ,						vocab_size=10_00    ,						)
														A      :					Dict      		=					CLIPTextModel(__UpperCAmelCase						)
														A      :					Dict      		=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

														A      :					int      		=					{
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''vae''': vae,
														    '''text_encoder''': text_encoder,
														    '''tokenizer''': tokenizer,
														    '''safety_checker''': None,
														    '''feature_extractor''': None,
														}
														return components




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=0						)		->       Dict:
														A      :					Any      		=					floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(__UpperCAmelCase						)						).to(__UpperCAmelCase						)
														A      :					Dict      		=					image.cpu().permute(0    ,						2    ,						3    ,						1						)[0]
														A      :					Optional[Any]      		=					Image.fromarray(np.uinta(__UpperCAmelCase						)						).convert('''RGB'''						)
														if str(__UpperCAmelCase						).startswith('''mps'''						):
																					A      :					str      		=					torch.manual_seed(__UpperCAmelCase						)
														else:
																					A      :					Optional[Any]      		=					torch.Generator(device=__UpperCAmelCase						).manual_seed(__UpperCAmelCase						)
														A      :					List[Any]      		=					{
														    '''prompt''': '''A painting of a squirrel eating a burger''',
														    '''image''': image,
														    '''generator''': generator,
														    '''num_inference_steps''': 2,
														    '''guidance_scale''': 6.0,
														    '''image_guidance_scale''': 1,
														    '''output_type''': '''numpy''',
														}
														return inputs




							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					Optional[Any]      		=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
														A      :					Dict      		=					self.get_dummy_components()
														A      :					str      		=					StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase						)
														A      :					Any      		=					sd_pipe.to(__UpperCAmelCase						)
														sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					Optional[Any]      		=					self.get_dummy_inputs(__UpperCAmelCase						)
														A      :					int      		=					sd_pipe(**__UpperCAmelCase						).images
														A      :					List[str]      		=					image[0, -3:, -3:, -1]
														assert image.shape == (1, 32, 32, 3)
														A      :					Optional[Any]      		=					np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]						)

														assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       int:
														A      :					Any      		=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
														A      :					Optional[Any]      		=					self.get_dummy_components()
														A      :					Any      		=					StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase						)
														A      :					str      		=					sd_pipe.to(__UpperCAmelCase						)
														sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					str      		=					self.get_dummy_inputs(__UpperCAmelCase						)
														A      :					Optional[int]      		=					'''french fries'''
														A      :					Any      		=					sd_pipe(**__UpperCAmelCase    ,						negative_prompt=__UpperCAmelCase						)
														A      :					Optional[int]      		=					output.images
														A      :					str      		=					image[0, -3:, -3:, -1]

														assert image.shape == (1, 32, 32, 3)
														A      :					Any      		=					np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]						)

														assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       Tuple:
														A      :					List[Any]      		=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
														A      :					List[Any]      		=					self.get_dummy_components()
														A      :					Optional[int]      		=					StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase						)
														A      :					Tuple      		=					sd_pipe.to(__UpperCAmelCase						)
														sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					str      		=					self.get_dummy_inputs(__UpperCAmelCase						)
														A      :					int      		=					[inputs['''prompt''']] * 2

														A      :					List[str]      		=					np.array(inputs['''image''']						).astype(np.floataa						) / 2_5_5.0
														A      :					Any      		=					torch.from_numpy(__UpperCAmelCase						).unsqueeze(0						).to(__UpperCAmelCase						)
														A      :					Union[str, Any]      		=					image / 2 + 0.5
														A      :					str      		=					image.permute(0    ,						3    ,						1    ,						2						)
														A      :					List[str]      		=					image.repeat(2    ,						1    ,						1    ,						1						)

														A      :					Union[str, Any]      		=					sd_pipe(**__UpperCAmelCase						).images
														A      :					int      		=					image[-1, -3:, -3:, -1]

														assert image.shape == (2, 32, 32, 3)
														A      :					Any      		=					np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]						)

														assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       Dict:
														A      :					int      		=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
														A      :					List[Any]      		=					self.get_dummy_components()
														A      :					Union[str, Any]      		=					EulerAncestralDiscreteScheduler(
														    beta_start=0.0_0_0_8_5    ,						beta_end=0.0_1_2    ,						beta_schedule='''scaled_linear'''						)
														A      :					int      		=					StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase						)
														A      :					Tuple      		=					sd_pipe.to(__UpperCAmelCase						)
														sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					Tuple      		=					self.get_dummy_inputs(__UpperCAmelCase						)
														A      :					Optional[Any]      		=					sd_pipe(**__UpperCAmelCase						).images
														A      :					Dict      		=					image[0, -3:, -3:, -1]

														A      :					Optional[int]      		=					[round(__UpperCAmelCase    ,						4						) for x in image_slice.flatten().tolist()]
														print(''','''.join([str(__UpperCAmelCase						) for x in slice]						)						)

														assert image.shape == (1, 32, 32, 3)
														A      :					Dict      		=					np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]						)

														assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       Union[str, Any]:
														super().test_inference_batch_single_identical(expected_max_diff=3E-3						)






							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					int      		=					self.get_dummy_components()
														A      :					int      		=					StableDiffusionInstructPixaPixPipeline(**__UpperCAmelCase						)
														A      :					Any      		=					VaeImageProcessor(do_resize=__UpperCAmelCase    ,						do_normalize=__UpperCAmelCase						)
														A      :					Optional[Any]      		=					pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					Union[str, Any]      		=					pipe(**self.get_dummy_inputs_by_type(__UpperCAmelCase    ,						input_image_type='''pt'''						)						)[0]

														A      :					Optional[Any]      		=					components['''vae''']
														A      :					Dict      		=					self.get_dummy_inputs_by_type(__UpperCAmelCase    ,						input_image_type='''pt'''						)

														for image_param in self.image_latents_params:
																					if image_param in inputs.keys():
																												A      :					Optional[Any]      		=					vae.encode(inputs[image_param]						).latent_dist.mode()

														A      :					List[str]      		=					pipe(**__UpperCAmelCase						)[0]

														A      :					Any      		=					np.abs(out - out_latents_inputs						).max()
														self.assertLess(__UpperCAmelCase    ,						1E-4    ,						'''passing latents as image input generate different result from passing image'''						)







@slow
@require_torch_gpu
class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       str:
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()




							def       snake_case			(					self    ,						__UpperCAmelCase=0						)		->       Tuple:
														A      :					List[str]      		=					torch.manual_seed(__UpperCAmelCase						)
														A      :					List[str]      		=					load_image(
														    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''						)
														A      :					Dict      		=					{
														    '''prompt''': '''turn him into a cyborg''',
														    '''image''': image,
														    '''generator''': generator,
														    '''num_inference_steps''': 3,
														    '''guidance_scale''': 7.5,
														    '''image_guidance_scale''': 1.0,
														    '''output_type''': '''numpy''',
														}
														return inputs




							def       snake_case			(					self						)		->       Optional[int]:
														A      :					Union[str, Any]      		=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
														    '''timbrooks/instruct-pix2pix'''    ,						safety_checker=__UpperCAmelCase						)
														pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)
														pipe.enable_attention_slicing()

														A      :					Optional[int]      		=					self.get_inputs()
														A      :					Any      		=					pipe(**__UpperCAmelCase						).images
														A      :					Union[str, Any]      		=					image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 5_12, 5_12, 3)
														A      :					Dict      		=					np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]						)

														assert np.abs(expected_slice - image_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       Dict:
														A      :					str      		=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
														    '''timbrooks/instruct-pix2pix'''    ,						safety_checker=__UpperCAmelCase						)
														A      :					List[str]      		=					LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
														pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)
														pipe.enable_attention_slicing()

														A      :					Union[str, Any]      		=					self.get_inputs()
														A      :					Union[str, Any]      		=					pipe(**__UpperCAmelCase						).images
														A      :					str      		=					image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 5_12, 5_12, 3)
														A      :					Dict      		=					np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]						)

														assert np.abs(expected_slice - image_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       List[Any]:
														A      :					List[Any]      		=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
														    '''timbrooks/instruct-pix2pix'''    ,						safety_checker=__UpperCAmelCase						)
														A      :					List[str]      		=					DDIMScheduler.from_config(pipe.scheduler.config						)
														pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)
														pipe.enable_attention_slicing()

														A      :					str      		=					self.get_inputs()
														A      :					List[Any]      		=					pipe(**__UpperCAmelCase						).images
														A      :					str      		=					image[0, -3:, -3:, -1].flatten()

														assert image.shape == (1, 5_12, 5_12, 3)
														A      :					Any      		=					np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]						)

														assert np.abs(expected_slice - image_slice						).max() < 1E-3




							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Optional[Any]      		=					0

														def callback_fn(__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						) -> None:
																					A      :					List[Any]      		=					True
																					nonlocal number_of_steps
																					number_of_steps += 1
																					if step == 1:
																												A      :					Optional[Any]      		=					latents.detach().cpu().numpy()
																												assert latents.shape == (1, 4, 64, 64)
																												A      :					Union[str, Any]      		=					latents[0, -3:, -3:, -1]
																												A      :					List[str]      		=					np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]						)

																												assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5E-2
																					elif step == 2:
																												A      :					int      		=					latents.detach().cpu().numpy()
																												assert latents.shape == (1, 4, 64, 64)
																												A      :					Optional[int]      		=					latents[0, -3:, -3:, -1]
																												A      :					str      		=					np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]						)

																												assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5E-2

														A      :					Optional[Any]      		=					False

														A      :					str      		=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
														    '''timbrooks/instruct-pix2pix'''    ,						safety_checker=__UpperCAmelCase    ,						torch_dtype=torch.floataa						)
														A      :					List[str]      		=					pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)
														pipe.enable_attention_slicing()

														A      :					List[Any]      		=					self.get_inputs()
														pipe(**__UpperCAmelCase    ,						callback=__UpperCAmelCase    ,						callback_steps=1						)
														assert callback_fn.has_been_called
														assert number_of_steps == 3




							def       snake_case			(					self						)		->       Tuple:
														torch.cuda.empty_cache()
														torch.cuda.reset_max_memory_allocated()
														torch.cuda.reset_peak_memory_stats()

														A      :					Optional[Any]      		=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
														    '''timbrooks/instruct-pix2pix'''    ,						safety_checker=__UpperCAmelCase    ,						torch_dtype=torch.floataa						)
														A      :					Dict      		=					pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)
														pipe.enable_attention_slicing(1						)
														pipe.enable_sequential_cpu_offload()

														A      :					Optional[Any]      		=					self.get_inputs()
														A      :					str      		=					pipe(**__UpperCAmelCase						)

														A      :					Dict      		=					torch.cuda.max_memory_allocated()
														# make sure that less than 2.2 GB is allocated
														assert mem_bytes < 2.2 * 10**9






							def       snake_case			(					self						)		->       int:
														A      :					Union[str, Any]      		=					self.get_inputs()
														# resize to resolution that is divisible by 8 but not 16 or 32
														A      :					Union[str, Any]      		=					inputs['''image'''].resize((5_04, 5_04)						)

														A      :					str      		=					'''timbrooks/instruct-pix2pix'''
														A      :					Tuple      		=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
														    __UpperCAmelCase    ,						safety_checker=__UpperCAmelCase    ,						)
														pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)
														pipe.enable_attention_slicing()

														A      :					Tuple      		=					pipe(**__UpperCAmelCase						)
														A      :					List[str]      		=					output.images[0]

														A      :					List[Any]      		=					image[2_55:2_58, 3_83:3_86, -1]

														assert image.shape == (5_04, 5_04, 3)
														A      :					Any      		=					np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]						)

														assert np.abs(image_slice.flatten() - expected_slice						).max() < 5E-3






